Mode-based Approaches to Estimating FreeSurfer Reconstruction Quality
Poster No:
1717
Submission Type:
Abstract Submission
Authors:
Alexander Holmes1, Francis Normand2, Mehul Gajwani2, Arshiya Sangchooli2, Linette Young2, Patricia Lee2, James Pang2, Kevin Aquino3, Alex Fornito2
Institutions:
1University of Oxford, Oxford, Oxfordshire, 2Monash University, Melbourne, Victoria, 3BrainKey, San Francisco, CA
First Author:
Co-Author(s):
Introduction:
Following predictions from neural field theory (Robinson et al., 2016), recent work has shown that modal decompositions of cortical geometry, termed eigenmodes, reliably capture diverse patterns of neural activity (Pang et al., 2023). Geometric eigenmodes are typically derived from cortical surfaces produced by FreeSurfer, an MRI software package that automatically segments and reconstructs cortical surfaces based on tissue boundaries within the brain (Fischl, 2012). Unfortunately, imaging artefacts can reduce FreeSurfer's reconstruction accuracy, as head motion and other sources of noise lead to the inclusion of non-brain tissue within the reconstructed cortical surface. These errors necessitate the application of manual edits on the cortical surface to correct for topological defects; however, edits are time consuming, causing many studies to omit performing edits for quality control (Alfaro Almagro et al., 2018). Although edits directly change estimates of cortical geometry, it is currently unknown whether geometric eigenmodes are robust to the application of such corrections, and by proxy, poor reconstruction quality. Accordingly, we aimed to investigate the effects of FreeSurfer reconstruction quality on the stability of geometric eigenmodes by comparing within-subject eigenmodes before and after the application of manual edits to the cortical surface.
Methods:
We assessed 503 individuals from the UK BioBank population study, using FreeSurfer's recon-all pipeline to obtain automated reconstructions of surface geometry for each subject. We applied manual edits on each subjects' cortical surfaces to correct for segmentation errors and surface defects (Fig. 1), while also rating reconstruction quality in each subject as either Excellent, Good, and Poor. From both the pre- and post-edited surfaces, we obtained geometric eigenmodes across the whole brain by solving the eigenvalue problem for the Laplace–Beltrami operator (LBO) of the midthickness cortical surface, where we retained the first 1000 eigenmodes. To assess within-subject similarity in eigenmode profiles, we measured correlations between each subject's pre- and post-edited eigenmodes, with greater correlations suggesting increased similarity. Mean absolute correlations for each subject were then compared across reconstruction quality groups via one-way ANOVA.
Results:
There was a statistically significant effect of reconstruction quality on eigenmode similarity, F(2,500)=13.51, p<.001, η2=.06 (Fig. 2A). Post-hoc comparisons using Tukey's HSD test revealed Poor quality scans (M=0.623, SD=0.063) had significantly lower eigenmode similarity than both Excellent (M=0.660, SD=0.056, p<.001) and Good scans (M=0.650, SD=0.046, p<.001), suggesting the effect of manual edits on geometric eigenmodes became more pronounced with worse reconstruction quality. These effects were primarily driven by distorted spatial maps in higher-frequency modes; eigenmode similarity was better preserved in lower-frequency modes across all groups, with similarity decreasing in higher-frequency modes (Excellent: r=-0.75; Good: r=-0.77; Poor: r=-0.74; Fig. 2B).
Conclusions:
Geometric eigenmodes are highly sensitive to FreeSurfer reconstruction quality. Scans with Poor reconstruction quality often necessitate manual edits to correct topological surface defects, causing reduced within-subject similarity in geometric eigenmodes before and after the application of edits. Even among higher-quality subjects, high-frequency modes were less stable than low-frequency modes, suggesting caution should be taken when interpreting these modes in isolation. Future work investigating geometric eigenmodes should adopt thorough quality control procedures within FreeSurfer to ensure robust and reliable results.
Modeling and Analysis Methods:
Exploratory Modeling and Artifact Removal
Image Registration and Computational Anatomy
Motion Correction and Preprocessing
Segmentation and Parcellation 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 1
Keywords:
Computational Neuroscience
Segmentation
STRUCTURAL MRI
Other - Geometric Eigenmodes; FreeSurfer
1|2Indicates the priority used for review
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Provide references using APA citation style.
Pang, J. C., Aquino, K. M., Oldehinkel, M., Robinson, P. A., Fulcher, B. D., Breakspear, M., & Fornito, A. (2023). Geometric constraints on human brain function. Nature, 618(7965), 566-574.
Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.
Alfaro-Almagro, F., Jenkinson, M., Bangerter, N. K., Andersson, J. L., Griffanti, L., Douaud, G., ... & Smith, S. M. (2018). Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage, 166, 400-424.